As our customer base grows and the number of production AI use-cases being monitored by Mona increases, our team has been working tirelessly to advance our product to become a best in class AI observability solution.
Posts about MLOps:
Last week, a draft of the EU’s highly anticipated Regulation on A European Approach For Artificial Intelligence was leaked. The official version is expected this week.
Just recently we published an important update on our growth, from recent customers to our team growth. Today, I’d like to go a little deeper on our current product and share how we’ve been expanding it in multiple areas to create value for our customers.
Making AI impactful and scalable is hard
In virtually every industry, companies invest heavily in AI. We all have an intuitive understanding of the “why”: Within...
Looking for a special gift for a data scientist or a data engineer at the office or in your life? You’ve come to the right place then. It’s just so hard to please them! We spent hours crunching data and running experiments with various gift ideas, narrowing the list to some of the best options so you won’t have to. Happy shopping!
AI teams across verticals vehemently agree that their data and models must be monitored in production. Yet, many teams struggle to define exactly what to monitor. Specifically, what data to collect “at inference time”, what metrics to track and how to analyze these metrics.